Scattered ray model derivation device, scattered ray model derivation method, scattered ray model derivation program, radiation image processing device, radiation image processing method, and radiation image processing program

ABSTRACT

At least one standard image representing a standard object having different thicknesses, the at least one standard image being obtained by imaging the standard object by radiation in a state in which an object is interposed between the standard object and a radiation detector is acquired, a relationship between the thickness of the standard object and a radiation attenuation coefficient of the standard object, which corresponds to an energy characteristic of the radiation, the relationship reflecting an influence of beam hardening by the standard object and the object, is derived, a primary ray component corresponding to the thickness of the standard object included in the standard image is derived based on the relationship between the thickness of the standard object and the radiation attenuation coefficient of the standard object, a scattered ray component corresponding to the thickness of the standard object included in the standard image is derived based on a difference between the standard image and the primary ray component, and a scattered ray model representing a relationship between the thickness of the standard object and a ratio of the scattered ray component to the primary ray component is derived.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application claims priority under 35 U.S.C. §119 to JapanesePatent Application No. 2021-037589, filed on Mar. 9, 2021. The aboveapplication is hereby expressly incorporated by reference, in itsentirety, into the present application.

BACKGROUND Technical Field

The present disclosure relates to a scattered ray model derivationdevice, a scattered ray model derivation method, a scattered ray modelderivation program, a radiation image processing device, a radiationimage processing method, and a radiation image processing program.

Related Art

In the related art, energy subtraction processing using two radiationimages obtained by irradiating a subject with two types of radiationhaving different energy distributions by using an amount of attenuationof transmitted radiation different from each other depending on asubstance configuring the subject is known. The energy subtractionprocessing is a method in which pixels of the two radiation imagesobtained as described above are associated with each other, and thepixels are multiplied by an appropriate weighting coefficient and thensubtracted (subtract) to acquire an image obtained by extracting aspecific structure included in the radiation image. By performing suchenergy subtraction processing, for example, in a case in which a softpart image obtained by extracting a soft part from the radiation imageacquired by imaging a chest is derived, a shadow appearing on the softpart can be observed without being disturbed by a bone. On the contrary,in a case in which a bone part image obtained by extracting the bonepart is derived, the shadow appearing on a bone part can be observedwithout being disturbed by the soft part.

In addition, various methods for deriving a composition of a human body,such as a bone mineral density, a fat, and a muscle, by the energysubtraction processing have also been proposed. For example,JP2018-153605A proposes a method in which a soft part image obtained byextracting a soft part of a subject is generated from a plurality ofradiation images acquired by radiation having different energydistributions transmitted through the subject, a body thicknessdistribution of the subject is estimated based on an imaging conditionin a case in which the soft part image and the radiation image areacquired, an approximate body thickness distribution that approximatesthe estimated body thickness distribution with a model corresponding toa human body is calculated, and a distribution of a body fat percentagein the subject is calculated based on the approximate body thicknessdistribution.

In addition, in a case of imaging the radiation image of the subject,particularly in a case in which the thickness of the subject is large,there is a problem that the radiation is scattered in the subject togenerate scattered rays, and the contrast of the acquired radiationimage is lowered by the generated scattered rays. Therefore, scatteredray removal processing for removing a scattered ray component includedin the radiation image is performed (see, for example, JP2015-043959A).Specifically, the scattered ray removal processing is performed byderiving the scattered ray component of the radiation image based on aradiation attenuation coefficient of the subject, and subtracting thederived scattered ray component from the radiation image. By applyingsuch scattered ray removal processing in a case of performing the energysubtraction processing, it is possible to acquire the bone part imageand the soft part image in which the composition is separated withhigher accuracy.

By the way, in a case in which radiation imaging of the subject isactually performed, a grid may be used between the subject and aradiation detector, or an object, such as an imaging table or a topplate of the imaging table may be interposed. Since such an object has aunique radiation characteristic, the radiation attenuation coefficientis changed due to beam hardening, which hardens a radiation quality bybeing transmitted through the object. Specifically, the beam hardeningreduces the radiation attenuation coefficient. In addition, theradiation attenuation coefficient is smaller as the energy of theradiation emitted to the subject is higher. Therefore, the compositionof the subject cannot be estimated with high accuracy unless an energycharacteristic of the radiation and the radiation characteristic of theobject interposed between the subject and the radiation detector aretaken into consideration.

SUMMARY OF THE INVENTION

The present invention has been made in view of the above circumstances,and is to estimate a composition of a subject with high accuracy.

An aspect of the present disclosure relates to a scattered ray modelderivation device comprising at least one processor, in which theprocessor acquires at least one standard image representing a standardobject having different thicknesses, the at least one standard imagebeing obtained by imaging the standard object by radiation in a state inwhich an object is interposed between the standard object and aradiation detector, derives a relationship between the thickness of thestandard object and a radiation attenuation coefficient of the standardobject, which corresponds to an energy characteristic of the radiation,the relationship reflecting an influence of beam hardening by thestandard object and the object, derives a primary ray componentcorresponding to the thickness of the standard object included in thestandard image based on the relationship between the thickness of thestandard object and the radiation attenuation coefficient of thestandard object, derives a scattered ray component corresponding to thethickness of the standard object included in the standard image based ona difference between the standard image and the primary ray component,and derives a scattered ray model representing a relationship betweenthe thickness of the standard object and a ratio of the scattered raycomponent to the primary ray component.

The “standard object having different thicknesses” may be one standardobject consisting of a plurality of portions having differentthicknesses, or may be a plurality of the standard objects havingdifferent thicknesses.

Note that in the scattered ray model derivation device according to theaspect of the present disclosure, the processor may derive therelationship between the thickness of the standard object and theradiation attenuation coefficient of the standard object based on theenergy characteristic of the radiation, the radiation attenuationcoefficient of the standard object which corresponds to the energycharacteristic, the thickness of the standard object, a radiationattenuation coefficient of the object which corresponds to the energycharacteristic, and a thickness of the object.

In addition, in the scattered ray model derivation device according tothe aspect of the present disclosure, the processor may derive therelationship between the thickness of the standard object and theradiation attenuation coefficient of the standard object based on theenergy characteristic of the radiation, the radiation attenuationcoefficient of the standard object which corresponds to the energycharacteristic, the thickness of the standard object, a radiationattenuation coefficient of the object which corresponds to the energycharacteristic, and the energy characteristics of the radiation beforeand after being transmitted through the object.

In addition, in the scattered ray model derivation device according tothe aspect of the present disclosure, the processor may derive therelationship between the thickness of the standard object and theradiation attenuation coefficient of the standard object for a thicknessthat is not present in the standard object by interpolating therelationship between the thickness of the standard object and theradiation attenuation coefficient of the standard object for a thicknessthat is present in the standard object, may derive the primary raycomponent for the thickness that is not present in the standard objectby interpolating the primary ray component for the thickness that ispresent in the standard object, may derive the scattered ray componentfor the thickness that is not present in the standard object byinterpolating the scattered ray component for the thickness that ispresent in the standard object, and may derive the scattered ray modelfor the thickness that is not present in the standard object byinterpolating the scattered ray model for the thickness that is presentin the standard object.

In addition, in the scattered ray model derivation device according tothe aspect of the present disclosure, the object may be at least one ofa top plate of an imaging table on which a subject is placed in animaging apparatus or a scattered ray removal grid for removing thescattered ray component from the radiation transmitted through thesubject.

Another aspect of the present disclosure relates to a radiation imageprocessing device comprising at least one processor, in which theprocessor acquires at least one radiation image of a subject, acquires abody thickness distribution of the subject, and derives at least oneprocessed radiation image by removing a scattered ray component includedin the radiation image by using the scattered ray model derived by thescattered ray model derivation device according to the aspect of thepresent disclosure and the body thickness distribution of the subject.

Note that in the radiation image processing device according to theaspect of the present disclosure, the processor may derive a primary raycomponent included in the at least one radiation image by using thescattered ray model and the body thickness distribution of the subject,and may derive the at least one processed radiation image by updatingthe body thickness distribution, the scattered ray component, and theprimary ray component until a difference between the primary raycomponent and the processed radiation image satisfies a predeterminedcondition.

In addition, in the radiation image processing device according to theaspect of the present disclosure, the radiation image of the subject maybe a first radiation image and a second radiation image based onradiation having different energy distributions, which is transmittedthrough the subject including a bone part and a soft part, and theprocessor may derive a first processed radiation image and a secondprocessed radiation image for the first radiation image and the secondradiation image, respectively, by removing the scattered ray componentfrom each of the first radiation image and the second radiation image byusing the scattered ray model, and may derive a composition of thesubject from the first processed radiation image and the secondprocessed radiation image.

In addition, in the radiation image processing device according to theaspect of the present disclosure, the processor may derive a bone partimage obtained by extracting the bone part of the subject from the firstprocessed radiation image and the second processed radiation image, andmay derive a bone mineral density as the composition for each pixel of abone region of the bone part image based on a pixel value of the bonepart image.

In addition, in the radiation image processing device according to theaspect of the present disclosure, the processor may derive the bonemineral density for each pixel of the bone region by correcting thepixel value of the bone part image by a correction coefficient derivedbased on a radiation attenuation coefficient of the bone part.

In this case, the correction coefficient may be also derived based on aradiation attenuation coefficient of an object interposed between thesubject and a radiation detector that acquires the first radiation imageand the second radiation image.

In addition, in the radiation image processing device according to theaspect of the present disclosure, the processor may derive a muscleimage obtained by extracting a muscle of the subject from the firstprocessed radiation image and the second processed radiation image, andmay derive a muscle mass as the composition for each pixel of the muscleimage based on a pixel value of the muscle image.

In addition, in the radiation image processing device according to theaspect of the present disclosure, the processor may derive a soft partimage obtained by extracting a soft part of the subject from the firstprocessed radiation image and the second processed radiation image, mayderive the muscle image from the soft part image, and may derive themuscle mass for each pixel of the muscle image by correcting the pixelvalue of the muscle image by a correction coefficient derived based on aradiation attenuation coefficient of the muscle.

In this case, the correction coefficient may be also derived based on aradiation attenuation coefficient of an object interposed between thesubject and a radiation detector that acquires the first radiation imageand the second radiation image.

In addition, in the radiation image processing device according to theaspect of the present disclosure, the processor may display thecomposition on a display.

Still another aspect of the present disclosure relates to a scatteredray model derivation method comprising acquiring at least one standardimage representing a standard object having different thicknesses, theat least one standard image being obtained by imaging the standardobject by radiation in a state in which an object is interposed betweenthe standard object and a radiation detector, deriving a relationshipbetween the thickness of the standard object and a radiation attenuationcoefficient of the standard object, which corresponds to an energycharacteristic of the radiation, the relationship reflecting aninfluence of beam hardening by the standard object and the object,deriving a primary ray component corresponding to the thickness of thestandard object included in the standard image based on the relationshipbetween the thickness of the standard object and the radiationattenuation coefficient of the standard object, deriving a scattered raycomponent corresponding to the thickness of the standard object includedin the standard image based on a difference between the standard imageand the primary ray component, and deriving a scattered ray modelrepresenting a relationship between the thickness of the standard objectand a ratio of the scattered ray component to the primary ray component.

Still another aspect of the present disclosure relates to a radiationimage processing method comprising acquiring at least one radiationimage of a subject, acquiring a body thickness distribution of thesubject, and deriving at least one processed radiation image by removinga scattered ray component included in the radiation image by using thescattered ray model derived by the scattered ray model derivation deviceaccording to the aspect of the present disclosure and the body thicknessdistribution of the subject.

Note that the scattered ray model derivation method and the radiationimage processing method according to the aspects of the presentdisclosure may be provided as a radiation image processing program to beexecuted by a computer.

According to the present disclosure, it is possible to estimate thecomposition of the subject with high accuracy.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic block diagram showing a configuration of aradiography system to which a scattered ray model derivation device anda radiation image processing device according to an embodiment of thepresent disclosure are applied.

FIG. 2 is a diagram showing a schematic configuration of the scatteredray model derivation device and the radiation image processing deviceaccording to the present embodiment.

FIG. 3 is a diagram showing a functional configuration of the scatteredray model derivation device and the radiation image processing deviceaccording to the present embodiment.

FIG. 4 is a diagram for describing imaging of a standard object.

FIG. 5 is a diagram showing a spectrum of radiation.

FIG. 6 is a diagram showing radiation attenuation coefficients of a softtissue, a bone tissue, and aluminum of a human body with respect toradiation energy.

FIG. 7 is a diagram showing a relationship between a thickness of thestandard object and the radiation attenuation coefficient.

FIG. 8 shows a scattered ray model.

FIG. 9 is a diagram showing a bone part image.

FIG. 10 is a diagram showing a soft part image.

FIG. 11 is a diagram showing a relationship of a contrast between a bonepart and a soft part with respect to a body thickness of a subject.

FIG. 12 is a diagram showing an example of a look-up table.

FIG. 13 is a diagram showing an example of energy spectra of radiationafter being transmitted through a muscle tissue and radiation afterbeing transmitted through a fat tissue.

FIG. 14 is a diagram showing a display screen of a bone mineral densityand a muscle mass.

FIG. 15 is a flowchart of scattered ray model derivation processingperformed in the present embodiment.

FIG. 16 is a flowchart of radiation image processing performed in thepresent embodiment.

DETAILED DESCRIPTION

In the following, an embodiment of the present disclosure will bedescribed with reference to the drawings. FIG. 1 is a schematic blockdiagram showing a configuration of a radiography system to which ascattered ray model derivation device and a radiation image processingdevice according to the embodiment of the present disclosure areapplied. As shown in FIG. 1, the radiography system according to thepresent embodiment comprises an imaging apparatus 1, a scattered raymodel derivation device and a radiation image processing device(hereinafter may be represented by the radiation image processingdevice) 10 according to the present embodiment.

The imaging apparatus 1 is an imaging apparatus that performs energysubtraction by a so-called one-shot method of converting radiation, suchas X-rays, emitted from a radiation source 2 and transmitted through asubject H who lies on an imaging table 3 into energy and irradiating afirst radiation detector 5 and a second radiation detector 6 with theconverted radiation. At the time of imaging, as shown in FIG. 1, ascattered ray removal grid (hereinafter simply referred to as a grid) 4,the first radiation detector 5, a radiation energy conversion filter 7made of a copper plate or the like, and the second radiation detector 6are disposed in order from a side closest to the radiation source 2, andthe radiation source 2 is driven. The first and second radiationdetectors 5 and 6 are closely attached to the radiation energyconversion filter 7. Note that the grid 4, the first radiation detector5, the radiation energy conversion filter 7, and the second radiationdetector 6 are attachably and detachably attached below a top plate 3Aof the imaging table 3 by an attachment portion 3B.

As a result, in the first radiation detector 5, a first radiation imageG1 of the subject H by low-energy radiation including so-called softrays is acquired. In addition, in the second radiation detector 6, asecond radiation image G2 of the subject H by high-energy radiation fromwhich the soft rays are removed is acquired. The first and secondradiation images G1 and G2 are input to the radiation image processingdevice 10.

The first and second radiation detectors 5 and 6 can perform recordingand reading-out of the radiation image repeatedly. A so-calleddirect-type radiation detector that directly receives emission of theradiation and generates an electric charge may be used, or a so-calledindirect-type radiation detector that converts the radiation intovisible light and then converts the visible light into an electriccharge signal may be used. In addition, as a method for reading out aradiation image signal, it is desirable to use a so-called thin filmtransistor (TFT) readout method in which the radiation image signal isread out by turning a TFT switch on and off, or a so-called opticalreadout method in which the radiation image signal is read out byemission of read out light. However, other methods may also be usedwithout being limited to these methods.

Note that in the imaging apparatus 1, only one radiation detector may beattached to the attachment portion 3B to image the subject H.

The grid 4 is configured by lead that does not transmit the radiationand an interspace material, such as aluminum or fiber that easilytransmit the radiation which are disposed alternately with a fine griddensity of about 4.0 lines/mm. By using the grid 4, a scattered raycomponent of the radiation transmitted through the subject H can beremoved, but it cannot be completely removed. Therefore, the first andsecond radiation images G1 and G2 include a primary ray component of theradiation transmitted through the subject H as well as the scattered raycomponent.

Note that the primary ray component is a signal component having a pixelvalue represented by the radiation that reaches the radiation detectorwithout being scattered by the subject H in the radiation that istransmitted through the subject H. On the other hand, the scattered raycomponent is a signal component having a pixel value represented by theradiation that reaches the radiation detector by being scattered by thesubject H in the radiation that is transmitted through the subject H.

The radiation image processing device 10 is connected to an imagestorage system 9 via a network (not shown). The image storage system 9is a system that stores image data of the radiation image captured bythe imaging apparatus 1. The image storage system 9 extracts an imagecorresponding to a request from the radiation image processing device 10from the stored radiation image and transmits the extracted image to arequest source device. Specific examples of the image storage system 9include picture archiving and communication systems (PACS).

Then, the scattered ray model derivation device and the radiation imageprocessing device according to the present embodiment will be described.First, with reference to FIG. 2, a hardware configuration of thescattered ray model derivation device and the radiation image processingdevice according to the present embodiment will be described. As shownin FIG. 2, the radiation image processing device 10 is a computer, suchas a workstation, a server computer, and a personal computer, andcomprises a central processing unit (CPU) 11, a non-volatile storage 13,and a memory 16 as a transitory storage region. In addition, thescattered ray model derivation device and the radiation image processingdevice 10 comprise a display 14, such as a liquid crystal display, aninput device 15, such as a keyboard and a mouse, and a network interface(I/F) 17 connected to a network (not shown). The CPU 11, the storage 13,the display 14, the input device 15, the memory 16, and the network I/F17 are connected to a bus 18. Note that the CPU 11 is an example of aprocessor according to the present disclosure.

The storage 13 is realized by a hard disk drive (HDD), a solid statedrive (SSD), a flash memory, and the like. The storage 13 as a storagemedium stores a scattered ray model derivation program 12A and aradiation image processing program 12B installed in the scattered raymodel derivation device and the radiation image processing device 10.The CPU 11 reads out the scattered ray model derivation program 12A andthe radiation image processing program 12B from the storage 13 andexpands the read out scattered ray model derivation program 12A andradiation image processing program 12B in the memory 16, and executesthe expanded scattered ray model derivation program 12A and radiationimage processing program 12B.

Note that the scattered ray model derivation program 12A and theradiation image processing program 12B are stored in a storage device ofthe server computer connected to the network or in a network storage ina state of being accessible from the outside, and are downloaded andinstalled in the computer that configures the scattered ray modelderivation device and the radiation image processing device 10 inresponse to the request. Alternatively, the scattered ray modelderivation program 12A and the radiation image processing program 12Bare distributed in a state of being recorded on a recording medium, suchas a digital versatile disc (DVD) or a compact disc read only memory(CD-ROM), and are installed in the computer that configures thescattered ray model derivation device and the radiation image processingdevice 10 from the recording medium.

Then, a functional configuration of the scattered ray model derivationdevice and the radiation image processing device according to thepresent embodiment will be described. FIG. 3 is a diagram showing thefunctional configuration of the scattered ray model derivation deviceand the radiation image processing device according to the presentembodiment. As shown in FIG. 3, the scattered ray model derivationdevice and the radiation image processing device 10 comprise an imageacquisition unit 21, an information acquisition unit 22, an attenuationcoefficient derivation unit 23, a primary ray component derivation unit24, a scattered ray component derivation unit 25, a model derivationunit 26, a scattered ray removal unit 27, a subtraction unit 28, acomposition derivation unit 29, and a display controller 30. Further, byexecuting the scattered ray model derivation program 12A, the CPU 11functions as the image acquisition unit 21, the information acquisitionunit 22, the attenuation coefficient derivation unit 23, the primary raycomponent derivation unit 24, the scattered ray component derivationunit 25, and the model derivation unit 26. In addition, by executing theradiation image processing program 12B, the CPU 11 functions as theimage acquisition unit 21, the scattered ray removal unit 27, thesubtraction unit 28, the composition derivation unit 29, and the displaycontroller 30.

In a case of deriving the scattered ray model, the image acquisitionunit 21 acquires a standard image K0 by causing the imaging apparatus 1to image a standard object simulating a human body. In this case, onlyone radiation detector is used. Note that in a case in which thestandard image K0 is stored in the image storage system 9, the imageacquisition unit 21 acquires the standard image K0 from the imagestorage system 9.

FIG. 4 is a diagram for describing imaging of the standard object. Asshown in FIG. 4, a standard object 35 is made of a material havingdifferent thickness portions, such as 5 cm, 10 cm, and 20 cm, in stagesand having the same radiation transmittance as the soft tissue (fat andmuscle) of the human body. Therefore, the standard object 35 simulates aradiation characteristic of the human body. Here, the soft tissue is amixture of the muscle and the fat in a certain ratio. A mixing ratio ofthe muscle and the fat differs depending on gender, physique, and thelike, but can be defined by an average body fat percentage (25%).Therefore, a material, such as acrylic, which corresponds to thecomposition mixed at a ratio of 0.75 of the muscle and 0.25 of the fat,is used as the standard object.

In a case of acquiring the standard image K0, as shown in FIG. 4, thestandard object 35 is placed on the top plate 3A of the imaging table 3,the radiation source 2 is driven to emit the radiation to the radiationdetector (here, the first radiation detector 5) via the grid 4, so thatthe image acquisition unit 21 acquires the standard image K0. The pixelvalue of each pixel of the standard image K0 includes the primary raycomponent based on the radiation traveling straight through the standardobject 35 and the scattered ray component based on the radiationscattered by the standard object 35.

Note that the standard object 35 is not limited to one object havingdifferent thicknesses as shown in FIG. 4. A plurality of the standardobjects having different thicknesses may be used. In this case, thestandard image K0 may be acquired by imaging the plurality of standardobjects at once, or the standard images corresponding to each of thestandard objects may be acquired by imaging the plurality of standardobjects separately.

On the other hand, in a case of deriving the composition of the subjectH as described below, the image acquisition unit 21 acquires the firstradiation image G1 and the second radiation image G2 which are the frontimages of the subject H from the first and second radiation detectors 5and 6 by causing the imaging apparatus 1 to image the subject H. Notethat in a case in which the first radiation image G1 and the secondradiation image G2 are stored in the image storage system 9, the imageacquisition unit 21 acquires the first radiation image G1 and the secondradiation image G2 from the image storage system 9.

In a case of acquiring the standard image K0, the first radiation imageG1, and the second radiation image G2, imaging conditions, such as animaging dose, a tube voltage, a source image receptor distance (SID),which is a distance between the radiation source 2 and the surfaces ofthe first and second radiation detectors 5 and 6, and the presence orabsence of the grid 4, are set.

The information acquisition unit 22 acquires the imaging conditions setat the time of imaging. In addition, the information acquisition unit 22acquires the energy characteristic of the radiation at the time ofimaging the standard object 35 in order to derive the scattered raymodel. The energy characteristic of the radiation may be acquired fromthe imaging apparatus 1, or the energy characteristic of the radiationmay be stored in the image storage system 9 and acquired from the imagestorage system 9. Note that a nominal value of the imaging apparatus 1may be used for the energy characteristic, since there are individualdifferences in the characteristic of the devices, it is preferable tomeasure the energy characteristic in advance by using a semiconductordosimeter.

Here, the energy characteristic is defined by any one of (i) a spectrumof the radiation emitted from the radiation source 2, (ii) the tubevoltage [kV] and a total filtration amount [mmAl equivalent], or (iii)the tube voltage [kV] and an aluminum half-valent layer [mmAl]. Thespectrum of the radiation is obtained by plotting a relationship betweenthe number of relative radiation photons with respect to the radiationenergy [keV]. The tube voltage means the maximum value of the generatedradiation energy distribution. The total filtration amount is obtainedby converting the filtration amount of each constituting component whichconfigures the imaging apparatus 1, such as a radiation generator and acollimator, in the radiation source 2 into a thickness of the aluminum.The influence of the beam hardening in the imaging apparatus 1 is largerand the total amount of high-energy components in the wavelengthdistribution of the radiation is larger as the total filtration amountis larger. The half-value layer is defined by the thickness of thealuminum necessary to attenuate the dose in half with respect to thegenerated radiation energy distribution. High-energy components in thewavelength distribution of the radiation is larger as the aluminum inthe half-value layer is thicker.

FIG. 5 is a diagram showing a spectrum of the radiation. In FIG. 5, thespectrum corresponds to the tube voltage of 90 kV and the totalfiltration amount of 2.5 mmAl. Note that the total filtration amount of2.5 mmAl corresponds to the half-value layer 2.96 mmAl.

By using the energy characteristic of the radiation, the attenuationcoefficient derivation unit 23 derives a relationship between thethickness of the standard object 35 and the radiation attenuationcoefficient of the standard object 35, which reflects the influence ofthe beam hardening of the object present between the standard object 35and the radiation detector 5.

The attenuation coefficient derivation unit 23 first derives the energyspectrum of the radiation from the radiation energy characteristicacquired by the information acquisition unit 22 by using a well-knownTucker approximation formula or the like. Note that the energycharacteristic acquired by the information acquisition unit 22 is theenergy spectrum of the radiation, the acquired energy spectrum need onlybe used as it is.

Moreover, the attenuation coefficient derivation unit 23 derives theradiation attenuation coefficient depending on the thickness of thestandard object 35 by simulating the spectrum of the radiation by usinga radiation attenuation characteristic of the soft tissue of the humanbody.

Here, in a case in which the energy spectrum of the radiation emittedfrom the radiation source 2 is defined as Sin(E) and the thickness ofthe standard object 35 is defined as t, the radiation dose Xbody(t)after being transmitted through the standard object 35 can be calculatedby Expression (1) using a radiation attenuation characteristic μSoft(E)of the soft tissue of the human body. Note that the radiationattenuation coefficients of the soft tissue, the bone tissue, and thealuminum of the human body with respect to the radiation energy areknown as shown in FIG. 6. The aluminum is the interspace material forthe grid 4. Here, FIG. 6 also shows the radiation attenuationcoefficient of the acrylic (polymethyl methacrylate, PMMA), which is thematerial of the standard object 35. The radiation attenuationcoefficient of the acrylic is substantially the same as the radiationattenuation coefficient of the soft tissue of the human body, as shownin FIG. 6.

X _(body)(t)=∫₀ ^(∞) S _(in)(E)×exp{−μ_(Soft)(E)×t}dE  (1)

On the other hand, as shown in FIG. 1, in a case in which the standardobject 35 is imaged by the imaging apparatus 1, the top plate 3A and thegrid 4 are present between the standard object 35 and the radiationdetectors 5 and 6. A material of the top plate 3A is the acrylic and theinterspace material of the grid 4 is the aluminum. In a case in whichthe radiation attenuation coefficient of the acrylic is defined asμMMA(E), the thickness of the top plate 3A (that is, the thickness ofthe acrylic) is defined as tPMMA, the radiation attenuationcharacteristic of the aluminum is defined as μAl(E), and the thicknessof grid 4 (that is, the aluminum) is defined as tAl, an X-ray doseXout(t) after being transmitted through the top plate 3A and the grid 4is represented by Expression (2).

X _(out)(t)=∫₀ ^(∞) S _(in)(E)×exp{−μ_(Soft)(E)×t}×exp{−μ_(PMMA)(E)×t_(PMMA)}×exp{−μ_(Al)(E)×t _(Al) }dE  (2)

Note that in a case in which the material of the top plate 3A and theinterspace material of the grid 4 are unknown, the X-ray dose Xout(t)after being transmitted through the top plate 3A and the grid 4 cannotbe derived by Expression (2). In this case, the energy characteristic(kV, TF0) of the radiation emitted from the radiation source 2 and theenergy characteristic (kV, TF1) of the radiation after being transmittedthrough the top plate 3A and the grid 4 are measured using a dosimeter,and the X-ray dose Xout(t) after being transmitted through the top plate3A and the grid 4 can be derived by Expression (2-1) using the energycharacteristic (kV, TF0) and the energy characteristic (kV, TF1). Notethat the energy characteristic in Expression (2-1) represents the totalfiltration amount (mmAl equivalent) of the radiation emitted by acertain tube voltage [kV].

X _(out)(t)=∫₀ ^(∞) S _(in)(E)×exp{−μ_(Soft)(E)×t}×exp{−μ_(Al)(E)×(TF1−TF0)}dE  (2-1)

The radiation attenuation coefficient of the standard object 35 in theimaging system including the top plate 3A and the grid 4 is obtained byrepresenting an attenuation ratio of the radiation after beingtransmitted through the standard object 35 by an attenuation index asshown in Expression (3) with reference to the radiation dose in a casein which the standard object 35 is not present (that is, in a case inwhich the thickness of the standard object 35 is 0).

$\begin{matrix}{\frac{X_{out}(t)}{X_{out}(0)} = {\exp\left\{ {{- {µ_{Soft}(t)}} \times t} \right\}}} & (3)\end{matrix}$

By solving Expression (3) with respect to the radiation attenuationcoefficient μSoft(t) of the soft tissue as shown in Expression (4), arelationship between a thickness t of the standard object 35 and theradiation attenuation coefficient can be derived.

$\begin{matrix}{{µ_{Soft}(t)} = {- \frac{\ln\left\{ \frac{X_{out}(t)}{X_{out}(0)} \right\}}{t}}} & (4)\end{matrix}$

The standard object 35 has a plurality of different thicknesses instages. Therefore, the attenuation coefficient derivation unit 23derives the radiation attenuation coefficient by Expression (4) for eachof the plurality of thicknesses of the standard object 35. Further, theattenuation coefficient derivation unit 23 derives the relationshipbetween the thickness t of the standard object 35 and the radiationattenuation coefficient by performing an interpolation calculation usingthe radiation attenuation coefficient of the thickness present in thestandard object 35 for the radiation attenuation coefficient of thethickness that is not present in the standard object 35. FIG. 7 is adiagram showing a relationship between the thickness t of the standardobject 35 and the radiation attenuation coefficient. FIG. 7 shows therelationship between the thickness of the standard object 35 and theradiation attenuation coefficient in a case in which the tube voltage is90 kV and the total filtration amount is 2.5 mmAl. The attenuationcoefficient derivation unit 23 derives the relationship between thethickness of the standard object 35 and the radiation attenuationcoefficient for each energy characteristic of the radiation, and storesthe derived relationship in the storage 13.

The primary ray component derivation unit 24 derives the radiationattenuation coefficient corresponding to the thickness of the standardobject 35 based on the relationship between the thickness of thestandard object 35 and the radiation attenuation coefficient derived bythe attenuation coefficient derivation unit 23. Moreover, the primaryray component included in the standard image K0 is derived based on theradiation attenuation coefficient corresponding to the thickness of thestandard object 35.

Here, in a case in which the pixel value of each pixel of the standardimage K0 is defined as I0o(x,y), the thickness of the standard object 35corresponding to each pixel of the standard image K0 is defined asT0(x,y), and the radiation attenuation coefficient derived by Expression(4) with respect to the thickness T0(x,y) of each pixel of the standardimage K0 is defined as μSoft0(x,y), the primary ray component derivationunit 24 derives a primary ray component I0p(x,y) included in the pixelvalue of each pixel of the standard image K0 by Expression (5). Notethat since the standard object 35 has the plurality of thicknesses instages, the primary ray component derivation unit 24 derives the primaryray component I0p(x,y) for each thickness present in the standard object35. Note that the primary ray component derivation unit 24 may derivethe relationship between the thickness of the standard object 35 and theprimary ray component by performing the interpolation calculation usingthe primary ray component of the thickness that is present in thestandard object 35 for the primary ray component corresponding to thethickness that is not present in the standard object 35.

I0p(x,y)=I0o(x,y)×exp(−μSoft0(x,y)×T0(x,y))  (5)

The scattered ray component derivation unit 25 derives the scattered raycomponent included in the standard object 35 based on the differencebetween the pixel value of the standard image K0 and the primary raycomponent. That is, the scattered ray component derivation unit 25derives a scattered ray component I0s(x,y) by Expression (6). Note thatsince the standard object 35 has the plurality of thicknesses in stages,the scattered ray component I0s(x,y) corresponding to the stepwisethickness of the standard object 35 is derived. Note that the scatteredray component derivation unit 25 may derive the relationship between thethickness of the standard object 35 and the scattered ray component byperforming the interpolation calculation using the scattered raycomponent of the thickness that is present in the standard object 35 forthe scattered ray component corresponding to the thickness that is notpresent in the standard object 35.

I0s(x,y)=I0o(x,y)−I0p(x,y)  (6)

The model derivation unit 26 derives the scattered ray modelrepresenting the relationship between the thickness of the standardobject 35 and the ratio of the scattered ray component I0s(x,y) to theprimary ray component I0p(x,y). That is, the model derivation unit 26derives the scattered ray model by calculating the ratio (that is,I0s(x,y)/I0p(x,y)) of the scattered ray component I0s(x,y) to theprimary ray component I0p(x,y) as a scatter-to-primary ratio (STPR) foreach thickness of the standard object 35 and plotting a relationshipbetween the thickness of the standard object 35 and the STPR. Note thatsince the thicknesses of the standard object 35 are different in stages,the STPR at the thickness that is not present at the standard object 35need only be derived by the interpolation calculation using the STPR atthe thickness that is present in the standard object 35.

FIG. 8 is a diagram showing the scattered ray model. FIG. 8 shows arelationship between the thickness of the standard object 35 and theSTPR in a case in which the tube voltage is 90 kV and the totalfiltration amount is 2.5 mmAl. The model derivation unit 26 stores thederived scattered ray model in the storage 13. Note that the standardobject 35 simulates the radiation characteristic of the human body.Therefore, the scattered ray model shown in FIG. 8 represents therelationship between the thickness of the subject H and the STPR.

Note that the scattered ray model need only be derived for each energycharacteristic of the radiation that can be emitted by the radiationsource 2 of the imaging apparatus 1 and stored in the storage 13.

Then, the radiation image processing device according to the presentembodiment will be described.

The scattered ray removal unit 27 removes the scattered ray componentfrom each of the first radiation image G1 and the second radiation imageG2 acquired by the image acquisition unit 21 by using the scattered raymodel derived by the model derivation unit 26. In the following, theremoval of the scattered ray component will be described. As a methodfor removing the scattered ray component, for example, any method, suchas a method disclosed in JP2015-043959A, can be used. In the following,scattered ray removal processing in a case in which the method disclosedin JP2015-043959A is used will be described. Note that in the followingdescription, G1 and G2 will be used as reference numerals for the firstand second radiation images from which the scattered ray component isremoved.

First, the scattered ray removal unit 27 acquires a virtual model of thesubject H having an initial body thickness distribution Ts(x,y). Thevirtual model is data virtually representing the subject H of which abody thickness in accordance with the initial body thicknessdistribution Ts(x,y) is associated with a coordinate position of eachpixel of the first radiation image G1. Note that the virtual model ofthe subject H having the initial body thickness distribution Ts(x,y) isstored in the storage 13 in advance, but the virtual model may beacquired from an external server in which the virtual model is stored.

Next, as shown in Expression (7) and Expression (8), the scattered rayremoval unit 27 derives an estimated primary ray image Ip(x,y) obtainedby estimating a primary ray image obtained by imaging the virtual modeland an estimated scattered ray image Is(x,y) obtained by estimating ascattered ray image obtained by imaging the virtual model, based on thevirtual model. Moreover, as shown in Expression (9), the scattered rayremoval unit 27 derives an image obtained by composing the estimatedprimary ray image Ip(x,y) and the estimated scattered ray image Is(x,y)as an estimated image Im(x,y) obtained by estimating the first radiationimage G1 obtained by imaging the subject H.

Ip(x,y)=Io(x,y)×exp(−μSoft(x,y)×T(x,y))  (7)

Is(x,y)=Io(x,y)×STPR(T(x,y))*PSF(T(x,y))  (8)

Im(x,y)=Is(x,y)+Ip(x,y)  (9)

Here, (x,y) is a coordinate of a pixel position of the first radiationimage G1, Io(x,y) is a pixel value of the first radiation image G1 atthe pixel position (x,y), Ip(x,y) is the primary ray component at thepixel position (x,y), and Is(x,y) is the scattered ray component at thepixel position (x,y). Note that in a case of deriving the firstestimated image Im(x,y), the initial body thickness distribution Ts(x,y)is used as the body thickness distribution T(x,y) in Expression (7) andExpression (8). In addition, μSoft (T(x,y)) in Expression (7) is derivedby referring to the relationship between the thickness t of the standardobject 35 shown in FIG. 7 and the radiation attenuation coefficientderived by the attenuation coefficient derivation unit 23. In addition,the STPR (T(x,y)) in Expression (8) is derived by referring to thescattered ray model shown in FIG. 8 derived by the model derivation unit26 based on the energy characteristic of the radiation used at the timeof imaging the subject H.

In addition, the PSF (T(x,y)) in Expression (8) is a point spreadfunction representing the distribution of the scattered rays spreadingfrom one pixel depending on the body thickness distribution T(x,y), andis defined depending on the energy characteristic of the radiation. Inaddition, * is an operator representing a convolution operation. The PSFis also changed due to a distribution of irradiation fields in theimaging apparatus 1, a distribution of the compositions of the subjectH, the irradiation dose at the time of imaging, the tube voltage, animaging distance, the characteristics of the radiation detectors 5 and6, and the like. Therefore, the PSF need only be experimentally obtainedin advance for each energy characteristic of the radiation used by theimaging apparatus 1 in accordance with irradiation field information,subject information, the imaging condition, and the like, and stored inthe storage 13.

Next, the scattered ray removal unit 27 corrects the initial bodythickness distribution Ts(x,y) of the virtual model such that adifference between the estimated image Im and the first radiation imageG1 is small. The scattered ray removal unit 27 updates the bodythickness distribution T(x,y), the scattered ray component Is(x,y), andthe primary ray component Ip(x,y) by repeating the derivation of thebody thickness distribution T(x,y), the scattered ray component Is(x,y),and the primary ray component Ip(x,y) until a difference between theestimated image Im and the first radiation image G1 satisfies apredetermined termination condition. The scattered ray removal unit 27subtracts the scattered ray component Is(x,y) derived by Expression (8)from the first radiation image G1 in a case in which the terminationcondition is satisfied. As a result, the scattered ray componentincluded in the first radiation image G1 is removed.

On the other hand, the scattered ray removal unit 27 also performs thescattered ray removal processing on the second radiation image G2 in thesame manner as in the first radiation image G1.

The subtraction unit 28 performs energy subtraction processing to derivea bone part image Gb in which a bone part of the subject H is extractedand a soft part image Gs in which a soft part is extracted from thefirst and second radiation images G1 and G2, which are subjected to thescattered ray removal processing. Note that the first and secondradiation images G1 and G2 in the subsequent processing are processedradiation images from which the scattered ray component is removed.

In a case in which the bone part image Gb is derived, the subtractionunit 28 performs weighting subtraction between the corresponding pixelswith respect to the first and second radiation images G1 and G2 as shownin Expression (10) to generate the bone part image Gb in which the bonepart of the subject H included in each of the radiation images G1 and G2is extracted, as shown in FIG. 9. In Expression (10), β1 is a weightingcoefficient, and is set as a value capable of extracting the bone partof the subject H included in each of the radiation images G1 and G2 byExpression (10) based on the radiation attenuation coefficients of thebone tissue and the soft tissue. Note that a pixel value of each pixelin a bone region in the bone part image Gb is a bone part pixel value.

Gb(x,y)=G1(x,y)−β1×G2(x,y)  (10)

On the other hand, in a case in which the soft part image Gs is derived,the subtraction unit 28 performs weighting subtraction between thecorresponding pixels with respect to the first and second radiationimages G1 and G2 as shown in Expression (11) to generate the soft partimage Gs in which the soft part of the subject H included in each of theradiation images G1 and G2 is extracted, as shown in FIG. 10. InExpression (11), β2 is a weighting coefficient, and is set as a valuecapable of extracting the soft part of the subject H included in each ofthe radiation images G1 and G2 by Expression (11) based on the radiationattenuation coefficients of the bone tissue and the soft tissue.

Gs(x,y)=G1(x,y)−β2×G2(x,y)  (11)

Note that the soft part image Gs shows a soft region due to a softtissue of the subject H. In the present embodiment, the “soft tissue” ofthe subject H refers to a tissue other than a bone tissue, andspecifically includes a muscle tissue, a fat tissue, blood, and water.

The composition derivation unit 29 derives the composition of thesubject H. Specifically, the composition derivation unit 29 derives thebone mineral density as the composition for each pixel of the boneregion of the bone part image Gb based on the pixel value of the bonepart image Gb. In addition, the composition derivation unit 29 derivesthe muscle mass as the composition for each pixel of the soft region inthe soft part image Gs. First, the derivation of the bone mineraldensity will be described.

The composition derivation unit 29 derives the bone mineral density foreach pixel of the bone part image Gb. In the present embodiment, thecomposition derivation unit 29 derives a bone mineral density B byconverting each pixel value of the bone part image Gb into the pixelvalue of the bone part image acquired under standard imaging conditions.Specifically, the composition derivation unit 29 derives the bonemineral density by correcting each pixel value of the bone part image Gbby using a correction coefficient acquired from a look-up tabledescribed below.

Here, the contrast between the soft part and the bone part in theradiation image is lower as the tube voltage in the radiation source 2is higher and the energy of the radiation emitted from the radiationsource 2 is higher. In addition, in a procedure of the radiationtransmitted through the subject H, a low-energy component of theradiation is absorbed by the subject H, and beam hardening occurs inwhich the radiation energy is increased. The increase in the radiationenergy due to the beam hardening is larger as the body thickness of thesubject H is larger.

FIG. 11 is a diagram showing a relationship of the contrast between thebone part and the soft part with respect to the body thickness of thesubject H. Note that FIG. 11 shows the relationship of the contrastbetween the bone part and the soft part with respect to the bodythickness of the subject H at the three tube voltages of 80 kV, 90 kV,and 100 kV. As shown in FIG. 11, the contrast is lower as the tubevoltage is higher. In addition, in a case in which the body thickness ofthe subject H exceeds a certain value, the contrast is lower as the bodythickness is larger. Note that contrast between the bone part and thesoft part is higher as the pixel value of the bone region in the bonepart image Gb is larger. Therefore, the relationship shown in FIG. 11shifts to a higher contrast side as the pixel value of the bone regionin the bone part image Gb is increased.

In the present embodiment, the look-up table for acquiring thecorrection coefficient for correcting the difference in the contrastdepending on the tube voltage at the time of imaging and the reductionin the contrast due to the influence of the beam hardening in the bonepart image Gb is stored in the storage 13. The correction coefficient isthe coefficient for correcting each pixel value of the bone part imageGb.

FIG. 12 is a diagram showing an example of the look-up table stored inthe storage 13. In FIG. 12, a look-up table LUT1 in which the standardimaging condition is set to the tube voltage of 90 kV is shown. As shownin FIG. 12, in the look-up table LUT1, the correction coefficient is setto be larger as the tube voltage is higher and the body thickness of thesubject H is larger. In the example shown in FIG. 12, since the standardimaging condition is the tube voltage of 90 kV, the correctioncoefficient is 1 in a case in which the tube voltage is 90 kV and thebody thickness is 0. Note that although the look-up table LUT1 is shownin two dimensions in FIG. 12, the correction coefficient differsdepending on the pixel value of the bone region. Therefore, the look-uptable LUT1 is actually a three-dimensional table to which an axisrepresenting the pixel value of the bone region is added.

The composition derivation unit 29 extracts the body thicknessdistribution T(x,y) of the subject H and a correction coefficientC0(x,y) for each pixel depending on a set value of the tube voltage ofthe radiation source 2 included in the imaging condition stored in thestorage 13 from the look-up table LUT1. As the body thicknessdistribution T(x,y) of the subject H, the body thickness distributionT(x,y) in a case in which the termination condition is satisfied in thescattered ray removal unit 27 is used. Further, as shown in Expression(12), the composition derivation unit 29 multiplies the pixel valueGb(x,y) of each pixel of the bone region in the bone part image Gb bythe correction coefficient C0(x,y) to derive the bone mineral densityB(x,y) (g/cm²) for each pixel of the bone part image Gb. The bonemineral density B(x,y) derived in this way is acquired by imaging thesubject H by the tube voltage of 90 kV, which is the standard imagingcondition, and shows the pixel value of the bone region included in theradiation image from which the influence of the beam hardening isremoved.

B(x,y)=C0(x,y)×Gb(x,y)  (12)

Here, it is possible to derive the correction coefficient C0(x,y) asfollows. First, the radiation attenuation coefficient of the bone tissuedepending on the thickness of the soft tissue of the subject H isderived. A state is assumed in which the soft tissue and the bone tissueoverlap on a radiation transmission path, and in a case in which thethickness of the soft tissue is defined as tsoft, the radiationattenuation coefficient can be derived as a function depending on thethickness of the soft tissue. In a case in which the energy spectrum ofthe radiation emitted from the radiation source 2 is defined as Sin(E)and the thickness of the soft tissue of the subject H is defined astsoft, a radiation dose Xout1(tsoft) after being transmitted through thesubject H in a case in which the bone tissue is not present can becalculated by Expression (13) for each thickness t of the subject Husing the radiation attenuation characteristic μSoft(E) of the softtissue of the human body. Note that in Expression (13), as in Expression(2), the radiation attenuation coefficient of the object (that is, thetop plate 3A and the grid 4) that is present between the subject H andthe radiation detectors 5 and 6 is taken into consideration.

X _(out1)(t)=∫₀ ^(∞) S _(in)(E)×exp{−μ_(Soft)(E)×t_(Soft)}×exp{−μ_(PMMA)(E)×t _(PMMA)}×exp{−μ_(Al)(E)×t _(Al) }dE  (13)

A radiation dose Xout2(t) in a case in which the bone tissue is presentis derived by Expression (14) further using a radiation attenuationcoefficient μBone(E) of the bone tissue.

X _(out2)(t)=∫₀ ^(∞) S _(in)(E)×exp{−μ_(Soft)(E)×t _(Soft)−μ_(Bone)(E)×t_(Bone)}×exp{−μ_(PMMA)(E)×t _(PMMA)}×exp{−μ_(Al)(E)×t _(Al) }dE  (14)

The radiation attenuation coefficient of the bone tissue is obtained byrepresenting an attenuation ratio of the radiation dose due to the bonetissue by the attenuation index with reference to the radiation dose ina case in which the bone tissue is not present, as shown in Expression(15).

$\begin{matrix}{\frac{X_{{out}\; 2}(t)}{X_{{out}\; 1}(t)} = {\exp\left\{ {{- {µ_{Bone}(t)}} \times t_{Bone}} \right\}}} & (15)\end{matrix}$

By solving Expression (15) for μBone(t) as shown in Expression (16), therelationship between the thickness t of the subject H and the radiationattenuation coefficient of the bone tissue can be derived. Note thattBone is the thickness of the bone tissue.

$\begin{matrix}{{µ_{Bone}(t)} = {- \frac{\ln\left\{ \frac{X_{{out}\; 2}(t)}{X_{{out}\; 1}(t)} \right\}}{t_{Bone}}}} & (16)\end{matrix}$

A correction coefficient C0(t) is derived by Expression (17) in whicheach thickness of the subject H is multiplied by the reciprocal theattenuation coefficient of the bone region with reference to a bonecontrast of the attenuation coefficient μBone (tbase) of the bone regionat an average thickness tbase of the subject H defined depending on animaging part.

$\begin{matrix}{{C\; 0(t)} = \frac{µ_{Bone}\left( t_{base} \right)}{µ_{Bone}(t)}} & (17)\end{matrix}$

Then, the derivation of the muscle mass will be described. As describedabove, the soft tissue includes the muscle tissue, the fat tissue, theblood, and the water. In the present embodiment, a tissue other than thefat tissue in the soft tissue is regarded as the muscle tissue. That is,in the present embodiment, a non-fat tissue including the blood and thewater is included in the muscle tissue to be handled as the muscletissue.

The composition derivation unit 29 separates the muscle and the fat inthe soft part image Gs by using a difference in an energy characteristicbetween the muscle tissue and the fat tissue. Here, as shown in FIG. 13,the dose of the radiation after being transmitted through the subject His lower than the dose of the radiation before being incident on thesubject H, which is a human body. In addition, since the energy absorbedby the muscle tissue and the energy absorbed by the fat tissue isdifferent and radiation attenuation coefficients are different, theenergy spectra of the radiation after being transmitted through themuscle tissue and the radiation after being transmitted through the fattissue in the radiation after being transmitted through the subject Hare different. As shown in FIG. 13, the energy spectrum of the radiationtransmitted through the subject H and emitted to each of the firstradiation detector 5 and the second radiation detector 6 depends on abody composition of the subject H, specifically, a ratio between themuscle tissue and the fat tissue. Since the fat tissue is more likely totransmit the radiation than the muscle tissue, the dose of the radiationafter being transmitted through the human body is smaller in a case inwhich the ratio of the muscle tissue is larger than the ratio of the fattissue.

Therefore, the composition derivation unit 29 separates the muscle andthe fat in the soft part image Gs by using the difference in the energycharacteristic between the muscle tissue and the fat tissue describedabove. That is, the composition derivation unit 29 generates a muscleimage and a fat image from the soft part image Gs. In addition, thecomposition derivation unit 29 derives the muscle mass of each pixelbased on the pixel value of the muscle image.

Note that a specific method by which the composition derivation unit 29separates the muscle and the fat from the soft part image Gs is notlimited, but as an example, the composition derivation unit 29 generatesthe muscle image from the soft part image Gs by Expression (18) andExpression (19). Specifically, first, the composition derivation unit 29derives a muscle ratio rm(x,y) at each pixel position(x,y) in the softpart image Gs by Expression (18). Note that in Expression (18), μm is aweighting coefficient depending on the radiation attenuation coefficientof the muscle tissue, and μA is a weighting coefficient depending on theradiation attenuation coefficient of the fat tissue. In addition, Δ(x,y)indicates a concentration difference distribution. The concentrationdifference distribution is a distribution of a concentration change onthe image, which is seen from a concentration obtained by making theradiation reach the first radiation detector 5 and the second radiationdetector 6 without transmitted through the subject H. The distributionof the concentration change on the image is calculated by subtractingthe concentration of each pixel in the region of the subject H from theconcentration in a blank region obtained by directly emitting theradiation to the first radiation detector 5 and the second radiationdetector 6 in the soft part image Gs.

rm(x,y)={μA−Δ(x,y)/T(x,y)}/(μA−μm)  (18)

Moreover, the composition derivation unit 29 generates a muscle image Gmfrom the soft part image Gs by Expression (19).

Gm(x,y)=rm(x,y)×Gs(x,y)  (19)

Further, as shown in Expression (20), the composition derivation unit 29derives the muscle mass M(x,y) (g/cm²) for each pixel of the muscleimage Gm by multiplying each pixel (x,y) of the muscle image Gm by thecorrection coefficient C1(x,y) representing a relationship between apredetermined pixel value and the muscle mass.

M(x,y)=C1(x,y)×Gm(x,y)  (20)

Here, it is possible to derive the correction coefficient C1(x,y) asfollows. First, the bone attenuation coefficient depending on thethickness of the soft part of the subject H is derived. A state isassumed in which the fat and the muscle overlap on a radiationtransmission path, and in a case in which the thickness of the fat isdefined as tA, the radiation attenuation coefficient can be derived as afunction depending on the thickness of the fat.

In a case in which the energy spectrum of the radiation emitted from theradiation source 2 is defined as Sin(E) and the thickness of the fat ofthe subject H is defined as tA, a radiation dose Xout3(t) after beingtransmitted through the subject H in a case in which the muscle is notpresent can be calculated by Expression (21) for each thickness t of thesubject H using the radiation attenuation characteristic μA(E) of thefat of the human body. Note that in Expression (21), as in Expression(2), the radiation attenuation coefficient of the object (that is, thetop plate 3A and the grid 4) that is present between the subject H andthe radiation detectors 5 and 6 is taken into consideration.

X _(out3)(t)=∫₀ ^(∞) S _(in)(E)×exp{−μ_(A)(E)×t _(A)}×exp{−μ_(PMMA)(E)×t_(PMMA)}×exp{−μ_(Al)(E)×t _(Al) }dE  (21)

A radiation dose Xout4(t) in a case in which the muscle is present isderived by Expression (22) further using a radiation attenuationcoefficient μM(E) of the muscle.

X _(out4)(t)=∫₀ ^(∞) S _(in)(E)×exp{−μ_(A)(E)×t _(A)−μ_(M)(E)×t_(M)}×exp{−μ_(PMMA)(E)×t _(PMMA)}×exp{−μ_(Al)(E)×t _(Al) }dE  (22)

The radiation attenuation coefficient of the muscle is obtained byrepresenting an attenuation ratio of the radiation dose due to themuscle by the attenuation index with reference to the radiation dose ina case in which the muscle is not present, as shown in Expression (23).

$\begin{matrix}{\frac{X_{{out}\; 4}(t)}{X_{{out}\; 3}(t)} = {\exp\left\{ {{- {µ_{M}(t)}} \times t_{M}} \right\}}} & (23)\end{matrix}$

By solving Expression (23) for μM(t) as shown in Expression (24), therelationship between the thickness tA of the fat and the radiationattenuation coefficient of the muscle can be derived. Note that tM isthe thickness of the muscle.

$\begin{matrix}{{µ_{M}(t)} = {- \frac{\ln\left\{ \frac{X_{{out}\; 4}(t)}{X_{{out}\; 3}(t)} \right\}}{t_{M}}}} & (24)\end{matrix}$

A correction coefficient C1(t) is derived by Expression (25) in whicheach thickness of the subject H is multiplied by the reciprocal theradiation attenuation coefficient of the muscle with reference to amuscle contrast of the radiation attenuation coefficient μM (tbase) ofthe muscle at an average thickness tbase of the subject H defineddepending on an imaging part.

$\begin{matrix}{{C\; 1(t)} = \frac{µ_{M}\left( t_{base} \right)}{µ_{M}(T)}} & (25)\end{matrix}$

The display controller 30 displays the bone mineral density and themuscle mass derived by the composition derivation unit 29 on the display14. FIG. 14 is a diagram showing a display screen of the bone mineraldensity and the muscle mass. As shown in FIG. 14, the display screen 40has a bone mineral density display region 41 and a muscle mass displayregion 42.

The bone part image Gb is displayed in the bone mineral density displayregion 41. In the bone part image Gb, a pattern is added to the boneregion depending on the bone mineral density. Note that in FIG. 14, forthe sake of simplicity, the pattern representing the bone mineraldensity is added only to the femur. Below the bone mineral densitydisplay region 41, a reference 43 representing the magnitude of the bonemineral density for the added pattern is displayed. An operator caneasily recognize the bone mineral density by interpreting the bone partimage Gb while referring to the reference 43. Note that different colorsmay be added to the bone part image Gb depending on the bone mineraldensity instead of the pattern.

In addition, the muscle image Gm is displayed in the muscle mass displayregion 42. Note that for the sake of description, an outline of the boneregion is shown by a broken line in the muscle image Gm. In the muscleimage Gm, a pattern is added to the soft region depending on the musclemass. Note that in FIG. 14, for the sake of simplicity, a patternrepresenting the muscle mass is added only in the vicinity of the femur.Below the muscle mass display region 42, a reference 44 representing themagnitude of the muscle mass for the added pattern is displayed. Theoperator can easily recognize the muscle mass by interpreting the softpart image Gs while referring to the reference 44. Note that differentcolors may be added to the muscle image Gm depending on the muscle massinstead of the pattern.

Then, processing performed in the present embodiment will be described.FIG. 15 is a flowchart of scattered ray model derivation processingperformed in the present embodiment. First, the information acquisitionunit 22 acquires the energy characteristic of the radiation (step ST1),and the attenuation coefficient derivation unit 23 derives therelationship between the thickness of the standard object 35 and theradiation attenuation coefficient, which reflects the influence of thebeam hardening of the object (that is, the top plate 3A and the grid 4)present between the standard object 35 and the radiation detector 5 byusing the energy characteristic (attenuation coefficient derivation;step ST2). Then, the image acquisition unit 21 acquires the standardimage K0 by causing the imaging apparatus 1 to image the standard object35 (step ST3). Note that the processing of step ST3 may be performedbefore steps ST1 and ST2, or may be performed in parallel with theprocessing of steps ST1 and ST2.

Subsequently, the primary ray component derivation unit 24 derives theprimary ray component included in the standard image K0 based on theradiation attenuation coefficient depending on the thickness of thestandard object 35 (step ST4). Moreover, the scattered ray componentderivation unit 25 derives the scattered ray component included in thestandard image K0 (step ST5). Further, the model derivation unit 26derives the scattered ray model representing the relationship betweenthe thickness of the standard object 35 and the ratio of the scatteredray component to the primary ray component (step ST6), and terminatesthe scattered ray model derivation processing. The derived scattered raymodel is stored in the storage 13.

Then, radiation image processing performed in the present embodimentwill be described. FIG. 16 is a flowchart showing the radiation imageprocessing performed in the present embodiment. Note that the first andsecond radiation images G1 and G2 are acquired by imaging and stored inthe storage 13. In a case in which an instruction for starting theprocessing is input from the input device 15, the image acquisition unit21 acquires the first and second radiation images G1 and G2 from thestorage 13 (radiation image acquisition; step ST11). Then, the scatteredray removal unit 27 removes the scattered ray components from the firstand second radiation images G1 and G2 acquired by the image acquisitionunit 21 by using the scattered ray model derived by the model derivationunit 26 (step ST12). Subsequently, the subtraction unit 28 derives thebone part image Gb in which the bone part of the subject H is extractedand the soft part image Gs in which the soft part is extracted from thefirst and second radiation images G1 and G2 (step ST13).

Subsequently, the composition derivation unit 29 derives the bonemineral density for each pixel of the bone part image Gb (step ST14).Further, the composition derivation unit 29 derives the muscle image Gmfrom the soft part image Gs, and derives the muscle mass for each pixelof the muscle image Gm (step ST15). Further, the display controller 30displays the bone mineral density and the muscle mass on the display 14(step ST16), and terminates the processing.

As described above, in the present embodiment, by using the energycharacteristic of the radiation in the imaging apparatus 1, therelationship between the thickness of the standard object 35 and theradiation attenuation coefficient, which reflects the influence of thebeam hardening of the object, such as the top plate 3A and the grid 4,interposed between the subject H and the first and second radiationdetectors 5 and 6 is derived, the primary ray component and thescattered ray component depending on the thickness of the standardobject 35 included in the standard image K0 are derived based on thederived relationship, and the scattered ray model representing therelationship between the thickness of the standard object 35 and theratio of the scattered ray component to the primary ray component isderived.

Therefore, by removing the scattered ray component from the radiationimage acquired by imaging the subject H by using the derived scatteredray model, it is possible to remove the scattered ray component from theradiation image in consideration of the energy characteristic of theradiation and the object present between the subject H and the radiationdetectors 5 and 6. Further, by using the first and second radiationimages G1 and G2 from which the scattered ray component is removed, itis possible to estimate the composition of the subject H, such as thebone mineral density and the muscle mass, with high accuracy inconsideration of the energy characteristic of the radiation emitted tothe subject H and the object interposed between the subject and theradiation detector.

Note that the embodiment described above, the composition derivationunit 29 derives both the bone mineral density and the muscle mass as thecomposition, but the present disclosure is not limited to this. Thecomposition derivation unit 29 may derive only the bone mineral densityas the composition. In this case, the subtraction unit 28 need onlyderive the bone part image Gb from the first and second radiation imagesG1 and G2. In addition, the composition derivation unit 29 may deriveonly the muscle mass as the composition. In this case, the subtractionunit 28 need only derive the soft part image Gs from the first andsecond radiation images G1 and G2.

In addition, in the embodiment described above, the object interposedbetween the subject H and the radiation detectors 5 and 6 is the topplate 3A and the grid 4, but the present disclosure is not limited tothis. In a case in which the grid 4 is not used, the object interposedbetween the subject H and the radiation detectors 5 and 6 is only thetop plate 3A. In addition, in a case in which the subject H is imaged ina standing position instead of a lying down position, only the grid 4may be used without using the top plate 3A. In this case, the objectinterposed between the subject H and the radiation detectors 5 and 6 isonly the grid 4. In this way, in a case in which the object interposedbetween the subject H and the radiation detectors 5 and 6 is only thetop plate 3A or only the grid 4, the relationship between the thicknessof the standard object 35 and the radiation attenuation coefficient,which reflects the influence of the radiation attenuation coefficient ofonly the top plate 3A or the beam hardening of only the grid 4, needonly be derived.

In addition, in the embodiment described above, instead of deriving thecomposition of the subject H, only the scattered ray removal processingfor the radiation image may be performed. In this case, in a case of thescattered ray removal processing, the scattered ray model derived in thesame manner as in the embodiment described above need only be used.

In addition, in the embodiment described above, by performing the energysubtraction processing, the muscle image Gm obtained by extracting themuscles in the soft tissue of the subject H may be derived from thefirst and second radiation images G1 and G2 which are subjected to thescattered ray removal processing. In a case in which the muscle image Gmis derived, the subtraction unit 28 need only perform weightingsubtraction between the corresponding pixels with respect to the firstand second radiation images G1 and G2 as shown in Expression (26) togenerate the muscle image Gm in which the muscle of the subject Hincluded in each of the radiation images G1 and G2 is extracted. InExpression (26), β3 is a weighting coefficient, and is set as a valuecapable of extracting the muscle of the subject H included in each ofthe radiation images G1 and G2 by Expression (26) based on the radiationattenuation coefficients of the fat and the muscle.

Gm(x,y)=G1(x,y)−β3×G2(x,y)  (26)

In addition, in the embodiment described above, the first and secondradiation images G1 and G2 are acquired by the one-shot method in a casein which the energy subtraction processing is performed, but the presentdisclosure is not limited to this. The first and second radiation imagesG1 and G2 may be acquired by a so-called two-shot method for performingimaging twice. In a case of the two-shot method, a position of thesubject H included in the first radiation image G1 and the secondradiation image G2 may shift due to a body movement of the subject H.Therefore, in the first radiation image G1 and the second radiationimage G2, it is preferable to perform the processing according to thepresent embodiment after registration of the subject is performed. Asregistration processing, for example, a method disclosed inJP2011-255060A can be used. In the method disclosed in JP2011-255060A,for each of the first and second radiation images G1 and G2, a pluralityof first band images and a plurality of second band images representingstructures having different frequency bands are generated, amisregistration amount of the positions corresponding to each other inthe first band image and the second band image of the correspondingfrequency band is acquired, and the registration of the first radiationimage G1 and the second radiation image G2 is performed based on themisregistration amount.

In addition, in the embodiment described above, the scattered ray modelderivation processing and the radiation image processing are performedby using the radiation image acquired by the system that images thefirst and second radiation images G1 and G2 of the subject H by usingthe first and second radiation detectors 5 and 6, it is needless to saythat the technology of the present disclosure can be applied to even ina case in which the first and second radiation images G1 and G2 areacquired by using an accumulative phosphor sheet instead of theradiation detector. In this case, the first and second radiation imagesG1 and G2 need only be acquired by stacking two accumulative phosphorsheets, emitting the radiation transmitted through the subject H,accumulating and recording radiation image information of the subject Hin each of the accumulative phosphor sheets, and photoelectricallyreading the radiation image information from each of the accumulativephosphor sheets. Note that the two-shot method may also be used in acase in which the first and second radiation images G1 and G2 areacquired by using the accumulative phosphor sheet.

In addition, the radiation in the embodiment described above is notparticularly limited, and α-rays or γ-rays can be used in addition toX-rays.

In addition, in the embodiment described above, various processors shownbelow can be used as the hardware structures of processing units thatexecute various pieces of processing, such as the image acquisition unit21, the information acquisition unit 22, the attenuation coefficientderivation unit 23, the primary ray component derivation unit 24, thescattered ray component derivation unit 25, the model derivation unit26, the scattered ray removal unit 27, the subtraction unit 28, thecomposition derivation unit 29, and the display controller 30. Asdescribed above, the various processors include, in addition to the CPUthat is a general-purpose processor which executes software (program)and functions as various processing units, a programmable logic device(PLD) that is a processor whose circuit configuration can be changedafter manufacture, such as a field programmable gate array (FPGA), and adedicated electric circuit that is a processor having a circuitconfiguration which is designed for exclusive use in order to execute aspecific processing, such as an application specific integrated circuit(ASIC).

One processing unit may be configured by one of these variousprocessors, or may be a combination of two or more processors of thesame type or different types (for example, a combination of a pluralityof FPGAs or a combination of the CPU and the FPGA). In addition, aplurality of the processing units may be configured by one processor.

As an example of configuring the plurality of processing units by oneprocessor, first, as represented by a computer, such as a client and aserver, there is an aspect in which one processor is configured by acombination of one or more CPUs and software and this processorfunctions as a plurality of processing units. Second, as represented bya system on chip (SoC) or the like, there is an aspect of using aprocessor that realizes the function of the entire system including theplurality of processing units by one integrated circuit (IC) chip. Inthis way, as the hardware structure, the various processing units areconfigured by using one or more of the various processors describedabove.

Moreover, as the hardware structures of these various processors, morespecifically, it is possible to use an electrical circuit (circuitry) inwhich circuit elements, such as semiconductor elements, are combined.

What is claimed is:
 1. A scattered ray model derivation devicecomprising: at least one processor, wherein the processor acquires atleast one standard image representing a standard object having differentthicknesses, the at least one standard image being obtained by imagingthe standard object by radiation in a state in which an object isinterposed between the standard object and a radiation detector, derivesa relationship between the thickness of the standard object and aradiation attenuation coefficient of the standard object, whichcorresponds to an energy characteristic of the radiation, therelationship reflecting an influence of beam hardening by the standardobject and the object, derives a primary ray component corresponding tothe thickness of the standard object included in the standard imagebased on the relationship between the thickness of the standard objectand the radiation attenuation coefficient of the standard object,derives a scattered ray component corresponding to the thickness of thestandard object included in the standard image based on a differencebetween the standard image and the primary ray component, and derives ascattered ray model representing a relationship between the thickness ofthe standard object and a ratio of the scattered ray component to theprimary ray component.
 2. The scattered ray model derivation deviceaccording to claim 1, wherein the processor derives the relationshipbetween the thickness of the standard object and the radiationattenuation coefficient of the standard object based on the energycharacteristic of the radiation, the radiation attenuation coefficientof the standard object which corresponds to the energy characteristic,the thickness of the standard object, a radiation attenuationcoefficient of the object which corresponds to the energycharacteristic, and a thickness of the object.
 3. The scattered raymodel derivation device according to claim 1, wherein the processorderives the relationship between the thickness of the standard objectand the radiation attenuation coefficient of the standard object basedon the energy characteristic of the radiation, the radiation attenuationcoefficient of the standard object which corresponds to the energycharacteristic, the thickness of the standard object, a radiationattenuation coefficient of the object which corresponds to the energycharacteristic, and the energy characteristics of the radiation beforeand after being transmitted through the object.
 4. The scattered raymodel derivation device according to claim 1, wherein the processorderives the relationship between the thickness of the standard objectand the radiation attenuation coefficient of the standard object for athickness that is not present in the standard object by interpolatingthe relationship between the thickness of the standard object and theradiation attenuation coefficient of the standard object for a thicknessthat is present in the standard object, derives the primary raycomponent for the thickness that is not present in the standard objectby interpolating the primary ray component for the thickness that ispresent in the standard object, derives the scattered ray component forthe thickness that is not present in the standard object byinterpolating the scattered ray component for the thickness that ispresent in the standard object, and derives the scattered ray model forthe thickness that is not present in the standard object byinterpolating the scattered ray model for the thickness that is presentin the standard object.
 5. The scattered ray model derivation deviceaccording to claim 1, wherein the object is at least one of a top plateof an imaging table on which a subject is placed in an imaging apparatusor a scattered ray removal grid for removing the scattered ray componentfrom the radiation transmitted through the subject.
 6. A radiation imageprocessing device comprising: at least one processor, wherein theprocessor acquires at least one radiation image of a subject, acquires abody thickness distribution of the subject, and derives at least oneprocessed radiation image by removing a scattered ray component includedin the radiation image by using the scattered ray model derived by thescattered ray model derivation device according to claim 1 and the bodythickness distribution of the subject.
 7. The radiation image processingdevice according to claim 6, wherein the processor derives a primary raycomponent included in the at least one radiation image by using thescattered ray model and the body thickness distribution of the subject,and derives the at least one processed radiation image by updating thebody thickness distribution, the scattered ray component, and theprimary ray component until a difference between the primary raycomponent and the processed radiation image satisfies a predeterminedcondition.
 8. The radiation image processing device according to claim6, wherein the radiation image of the subject is a first radiation imageand a second radiation image based on radiation having different energydistributions, which is transmitted through the subject including a bonepart and a soft part, and the processor derives a first processedradiation image and a second processed radiation image for the firstradiation image and the second radiation image, respectively, byremoving the scattered ray component from each of the first radiationimage and the second radiation image by using the scattered ray model,and derives a composition of the subject from the first processedradiation image and the second processed radiation image.
 9. Theradiation image processing device according to claim 8, wherein theprocessor derives a bone part image obtained by extracting the bone partof the subject from the first processed radiation image and the secondprocessed radiation image, and derives a bone mineral density as thecomposition for each pixel of a bone region of the bone part image basedon a pixel value of the bone part image.
 10. The radiation imageprocessing device according to claim 9, wherein the processor derivesthe bone mineral density for each pixel of the bone region by correctingthe pixel value of the bone part image by a correction coefficientderived based on a radiation attenuation coefficient of the bone part.11. The radiation image processing device according to claim 10, whereinthe correction coefficient is also derived based on a radiationattenuation coefficient of an object interposed between the subject anda radiation detector that acquires the first radiation image and thesecond radiation image.
 12. The radiation image processing deviceaccording to claim 8, wherein the processor derives a muscle imageobtained by extracting a muscle of the subject from the first processedradiation image and the second processed radiation image, and derives amuscle mass as the composition for each pixel of the muscle image basedon a pixel value of the muscle image.
 13. The radiation image processingdevice according to claim 12, wherein the processor derives a soft partimage obtained by extracting a soft part of the subject from the firstprocessed radiation image and the second processed radiation image,derives the muscle image from the soft part image, and derives themuscle mass for each pixel of the muscle image by correcting the pixelvalue of the muscle image by a correction coefficient derived based on aradiation attenuation coefficient of the muscle.
 14. The radiation imageprocessing device according to claim 13, wherein the correctioncoefficient is also derived based on a radiation attenuation coefficientof an object interposed between the subject and a radiation detectorthat acquires the first radiation image and the second radiation image.15. The radiation image processing device according to claim 8, whereinthe processor displays the composition on a display.
 16. A scattered raymodel derivation method comprising: acquiring at least one standardimage representing a standard object having different thicknesses, theat least one standard image being obtained by imaging the standardobject by radiation in a state in which an object is interposed betweenthe standard object and a radiation detector; deriving a relationshipbetween the thickness of the standard object and a radiation attenuationcoefficient of the standard object, which corresponds to an energycharacteristic of the radiation, the relationship reflecting aninfluence of beam hardening by the standard object and the object;deriving a primary ray component corresponding to the thickness of thestandard object included in the standard image based on the relationshipbetween the thickness of the standard object and the radiationattenuation coefficient of the standard object; deriving a scattered raycomponent corresponding to the thickness of the standard object includedin the standard image based on a difference between the standard imageand the primary ray component; and deriving a scattered ray modelrepresenting a relationship between the thickness of the standard objectand a ratio of the scattered ray component to the primary ray component.17. A radiation image processing method comprising: acquiring at leastone radiation image of a subject; acquiring a body thicknessdistribution of the subject; and deriving at least one processedradiation image by removing a scattered ray component included in theradiation image by using the scattered ray model derived by thescattered ray model derivation device according to claim 1 and the bodythickness distribution of the subject.
 18. A non-transitorycomputer-readable storage medium that stores a scattered ray modelderivation program causing a computer to execute: a procedure ofacquiring at least one standard image representing a standard objecthaving different thicknesses, the at least one standard image beingobtained by imaging the standard object by radiation in a state in whichan object is interposed between the standard object and a radiationdetector; a procedure of deriving a relationship between the thicknessof the standard object and a radiation attenuation coefficient of thestandard object, which corresponds to an energy characteristic of theradiation, the relationship reflecting an influence of beam hardening bythe standard object and the object; a procedure of deriving a primaryray component corresponding to the thickness of the standard objectincluded in the standard image based on the relationship between thethickness of the standard object and the radiation attenuationcoefficient of the standard object; a procedure of deriving a scatteredray component corresponding to the thickness of the standard objectincluded in the standard image based on a difference between thestandard image and the primary ray component; and a procedure ofderiving a scattered ray model representing a relationship between thethickness of the standard object and a ratio of the scattered raycomponent to the primary ray component.
 19. A non-transitorycomputer-readable storage medium that stores a radiation imageprocessing program causing a computer to execute: a procedure ofacquiring at least one radiation image of a subject; a procedure ofacquiring a body thickness distribution of the subject; and a procedureof deriving at least one processed radiation image by removing ascattered ray component included in the radiation image by using thescattered ray model derived by the scattered ray model derivation deviceaccording to claim 1 and the body thickness distribution of the subject.